How to Use the Donorbox MCP in Pydantic AI

Build type-safe fundraising pipelines using Pydantic AI to validate every Donorbox donor record and campaign audit via MCP.

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Connect Donorbox MCP to Pydantic AI

Create your Vinkius account to connect Donorbox to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Donorbox with Pydantic AI

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Validate Donorbox data structures with Pydantic AI

The `list_donorbox_donations` tool outputs structured transaction records that your agent validates at runtime. This MCP tool ensures that currency codes, donation amounts, and timestamps match your schemas exactly. If the API returns unexpected null values, the framework raises a validation error immediately. This prevents corrupted financial data from entering your internal accounting pipelines.

Monitor recurring plans with zero validation errors

The `list_recurring_donation_plans` tool returns active subscription structures directly to your validation layer. Your agent uses this tool to check subscription frequencies and payment methods. You can also call `list_active_subscription_plans` to filter out paused or failed memberships. This strict schema enforcement guarantees that your automated receipt emails always go to active supporters.

Run type-safe campaign performance audits

The `quick_campaign_performance_audit` tool gets high-level performance summaries validated against your strict Python models. Your agent parses these metrics to calculate conversion rates without risk of type errors. For deeper analysis, the agent calls `get_campaign_details` to fetch specific campaign targets. This ensures your internal reports always receive clean integer and float types.

Setup guide

Set up Donorbox MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "donorbox-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Donorbox tools.",
)

result = await agent.run("List recent Donorbox transactions")
print(result.output)

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Common questions about Donorbox MCP in Pydantic AI

Import the toolset from the Pydantic AI library and pass your Vinkius HTTP URL. Then, include this MCP Server toolset in your agent's configuration.

Your Pydantic AI agent will fail loudly with a validation error. This prevents the system from processing malformed campaign data or corrupted donor lists.

Yes. You can configure your agent to only register specific tools, such as `search_donors_by_email`, keeping sensitive donation details hidden.

Yes. You must run the MCP Server externally or use the managed Vinkius platform to expose the HTTP endpoints for your agent.

Your donor emails, campaign goals, and recurring payment statuses are processed entirely in-memory. Vinkius isolates the connection using token-based authentication and never stores your API payloads.

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